Lookup NU author(s): Dr Paolo Missier,
Dr Jacek Cala,
Dr Manisha Rathi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
© Springer International Publishing AG 2017. A pervasive problem in Data Science is that the knowledge generated by possibly expensive analytics processes is subject to decay over time as the data and algorithms used to compute it change, and the external knowledge embodied by reference datasets evolves. Deciding when such knowledge outcomes should be refreshed, following a sequence of data change events, requires problem-specific functions to quantify their value and its decay over time, as well as models for estimating the cost of their re-computation. Challenging is the ambition to develop a decision support system for informing re-computation decisions over time that is both generic and customisable.With the help of a case study from genomics, in this paper we offer an initial formalisation of this problem, highlight research challenges, and outline a possible approach based on the analysis of metadata from a history of past computations.
Author(s): Missier P, Cala J, Rathi M
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: BICOD 2017 31st British International Conference on Databases
Year of Conference: 2017
Print publication date: 14/06/2017
Online publication date: 14/06/2017
Acceptance date: 02/04/2016
Publisher: Springer Verlag
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)